Association of gastroesophageal reflux disease or laryngopharyngeal reflux disease and laryngeal squamous cell carcinoma:A Meta analysis
Bibliographic record
Abstract
OBJECTIVE:To use meta-analysis to synthetically evaluate the relationship between GERD and LSCC,which would provide a better scientific basis for the prevention and treatment of laryngeal squamous cell carcinoma.METHODS:The literatures from PubMed,Embase,Web of Knowledge,CNKI,Wanfang,CBM databases were searched according to the selection criteria.The Newcastle-Ottawa Scale(NOS)was used for assessing the quality of nonrandomized studies and the quality of included studies.The SAS software was used to calculate the p values of chi-square(χ 2)tests and fisher exact tests.The OR values of all studies were calculated after heterogeneity test with STATA 10software and the publication bias was evaluated at the same time. RESULTS:Nine studies were included according to the selection criteria.A total of 32 348larynx cancer patients and 86 694controls were included in this study.The prevalence of GERD in the LSCC group and control group were 21.0%and 11.0%separately.Each of 9studies included was high quality.The pooled OR of susceptibility to larynx cancer with GERD compared to control group was 2.01(95%CI:1.35-2.99,P0.001).The publication bias analysis had no statistically significant results.However,the subgroup analysis indicated the association between larynx cancer and GRED could only be found in Americans.The pooled OR value of the studies matched by confounding factors(OR=1.71,95%CI:1.06-2.76)was a little lower than those who were not matched.Excluding the two large researches,the conclusions were not affected.Sensitivity analysis showed that the conclusions of this study were stable.CONCLUSIONS:GRED seems to be associated with laryngeal squamous cell carcinoma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.074 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".